Driver-automation collaborative steering control for intelligent vehicles under unexpected emergency conditions

Most of fatal traffic accidents occur in unexpected emergency conditions, such as, post-impact, tire blowout etc, in which vehicle attitudes are immediately changed due to external disturbances and internal perturbations. It is an extremely challenging task for human driver to effectively and timely stop or control such a vehicle, especially for the inexperienced driver. Towards this end, this paper proposes a driver-automation collaborative control scheme for vehicles subjected to unexpected emergency conditions by assisting human driver's steering manipulation. To begin with, a model predictive lateral controller is constructed to enhance dynamical stability and collision avoidance capability considering model uncertainty and external disturbances. After that, a collaborative steering control authority allocator is designed for adaptively allocating control weighting of respective steering angles, in which the parameterized human driver activation is formulated considering driving action and state. As well, an optimal preview acceleration driver model combined with neuromuscular dynamics is developed for imitating human driver's steering manipulation while harmonizing with the controller. Lastly, simulation examples with different experienced human drivers validated the effectiveness and superiority of proposed control scheme and approaches in lateral stability enhancement and collision avoidance capability of intelligent vehicles subjected to unexpected emergency conditions.